Overview

Dataset statistics

Number of variables43
Number of observations119390
Missing cells18823
Missing cells (%)0.4%
Duplicate rows8175
Duplicate rows (%)6.8%
Total size in memory39.2 MiB
Average record size in memory344.0 B

Variable types

Categorical22
Numeric21

Alerts

Dataset has 8175 (6.8%) duplicate rowsDuplicates
country has a high cardinality: 177 distinct valuesHigh cardinality
reservation_status_date has a high cardinality: 926 distinct valuesHigh cardinality
arrival_date_week_number is highly overall correlated with arrival_date_month and 1 other fieldsHigh correlation
agent is highly overall correlated with hotelHigh correlation
CPI_AVG is highly overall correlated with CSMR_SENT and 9 other fieldsHigh correlation
INFLATION is highly overall correlated with FUEL_PRCS and 5 other fieldsHigh correlation
CSMR_SENT is highly overall correlated with CPI_AVG and 9 other fieldsHigh correlation
UNRATE is highly overall correlated with CPI_AVG and 9 other fieldsHigh correlation
GDP is highly overall correlated with CPI_AVG and 8 other fieldsHigh correlation
FUEL_PRCS is highly overall correlated with INFLATION and 5 other fieldsHigh correlation
CPI_HOTELS is highly overall correlated with CPI_AVG and 9 other fieldsHigh correlation
DIS_INC is highly overall correlated with CPI_AVG and 9 other fieldsHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
arrival_date_year is highly overall correlated with CPI_AVG and 10 other fieldsHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_number and 1 other fieldsHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
MO_YR is highly overall correlated with arrival_date_week_number and 13 other fieldsHigh correlation
INFLATION_CHG is highly overall correlated with CPI_AVG and 8 other fieldsHigh correlation
INTRSRT is highly overall correlated with CPI_AVG and 11 other fieldsHigh correlation
US_GINI is highly overall correlated with CPI_AVG and 10 other fieldsHigh correlation
children is highly imbalanced (80.7%)Imbalance
babies is highly imbalanced (97.2%)Imbalance
meal is highly imbalanced (53.5%)Imbalance
country is highly imbalanced (53.1%)Imbalance
distribution_channel is highly imbalanced (63.2%)Imbalance
is_repeated_guest is highly imbalanced (79.6%)Imbalance
reserved_room_type is highly imbalanced (58.3%)Imbalance
assigned_room_type is highly imbalanced (51.4%)Imbalance
deposit_type is highly imbalanced (65.3%)Imbalance
customer_type is highly imbalanced (50.6%)Imbalance
required_car_parking_spaces is highly imbalanced (85.4%)Imbalance
agent has 16340 (13.7%) missing valuesMissing
previous_cancellations is highly skewed (γ1 = 24.45804872)Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.53979995)Skewed
lead_time has 6345 (5.3%) zerosZeros
stays_in_weekend_nights has 51998 (43.6%) zerosZeros
stays_in_week_nights has 7645 (6.4%) zerosZeros
previous_cancellations has 112906 (94.6%) zerosZeros
previous_bookings_not_canceled has 115770 (97.0%) zerosZeros
booking_changes has 101314 (84.9%) zerosZeros
days_in_waiting_list has 115692 (96.9%) zerosZeros
adr has 1959 (1.6%) zerosZeros
total_of_special_requests has 70318 (58.9%) zerosZeros

Reproduction

Analysis started2023-10-22 16:18:12.714225
Analysis finished2023-10-22 16:20:56.113814
Duration2 minutes and 43.4 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
City Hotel
79330 
Resort Hotel
40060 

Length

Max length12
Median length10
Mean length10.671078
Min length10

Characters and Unicode

Total characters1274020
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 79330
66.4%
Resort Hotel 40060
33.6%

Length

2023-10-22T16:20:56.251668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:20:56.449985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
hotel 119390
50.0%
city 79330
33.2%
resort 40060
 
16.8%

Most occurring characters

ValueCountFrequency (%)
t 238780
18.7%
o 159450
12.5%
e 159450
12.5%
119390
9.4%
H 119390
9.4%
l 119390
9.4%
C 79330
 
6.2%
i 79330
 
6.2%
y 79330
 
6.2%
R 40060
 
3.1%
Other values (2) 80120
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 915850
71.9%
Uppercase Letter 238780
 
18.7%
Space Separator 119390
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 238780
26.1%
o 159450
17.4%
e 159450
17.4%
l 119390
13.0%
i 79330
 
8.7%
y 79330
 
8.7%
s 40060
 
4.4%
r 40060
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
H 119390
50.0%
C 79330
33.2%
R 40060
 
16.8%
Space Separator
ValueCountFrequency (%)
119390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1154630
90.6%
Common 119390
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 238780
20.7%
o 159450
13.8%
e 159450
13.8%
H 119390
10.3%
l 119390
10.3%
C 79330
 
6.9%
i 79330
 
6.9%
y 79330
 
6.9%
R 40060
 
3.5%
s 40060
 
3.5%
Common
ValueCountFrequency (%)
119390
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1274020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 238780
18.7%
o 159450
12.5%
e 159450
12.5%
119390
9.4%
H 119390
9.4%
l 119390
9.4%
C 79330
 
6.2%
i 79330
 
6.2%
y 79330
 
6.2%
R 40060
 
3.1%
Other values (2) 80120
 
6.3%

is_canceled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
75166 
1
44224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Length

2023-10-22T16:20:56.596403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:20:56.755562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring characters

ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119390
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

lead_time
Real number (ℝ)

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.01142
Minimum0
Maximum737
Zeros6345
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:20:56.924780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3160
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)142

Descriptive statistics

Standard deviation106.8631
Coefficient of variation (CV)1.027417
Kurtosis1.6964488
Mean104.01142
Median Absolute Deviation (MAD)60
Skewness1.3465499
Sum12417923
Variance11419.722
MonotonicityNot monotonic
2023-10-22T16:20:57.142033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6345
 
5.3%
1 3460
 
2.9%
2 2069
 
1.7%
3 1816
 
1.5%
4 1715
 
1.4%
5 1565
 
1.3%
6 1445
 
1.2%
7 1331
 
1.1%
8 1138
 
1.0%
12 1079
 
0.9%
Other values (469) 97427
81.6%
ValueCountFrequency (%)
0 6345
5.3%
1 3460
2.9%
2 2069
 
1.7%
3 1816
 
1.5%
4 1715
 
1.4%
5 1565
 
1.3%
6 1445
 
1.2%
7 1331
 
1.1%
8 1138
 
1.0%
9 992
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
2016
56707 
2017
40687 
2015
21996 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters477560
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 56707
47.5%
2017 40687
34.1%
2015 21996
 
18.4%

Length

2023-10-22T16:20:57.352761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:20:57.525727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2016 56707
47.5%
2017 40687
34.1%
2015 21996
 
18.4%

Most occurring characters

ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 477560
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 477560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
August
13877 
July
12661 
May
11791 
October
11160 
April
11089 
Other values (7)
58812 

Length

Max length9
Median length7
Mean length5.9031828
Min length3

Characters and Unicode

Total characters704781
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 13877
11.6%
July 12661
10.6%
May 11791
9.9%
October 11160
9.3%
April 11089
9.3%
June 10939
9.2%
September 10508
8.8%
March 9794
8.2%
February 8068
6.8%
November 6794
5.7%
Other values (2) 12709
10.6%

Length

2023-10-22T16:20:57.685078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 13877
11.6%
july 12661
10.6%
may 11791
9.9%
october 11160
9.3%
april 11089
9.3%
june 10939
9.2%
september 10508
8.8%
march 9794
8.2%
february 8068
6.8%
november 6794
5.7%
Other values (2) 12709
10.6%

Most occurring characters

ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 585391
83.1%
Uppercase Letter 119390
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 95619
16.3%
r 78190
13.4%
u 65351
11.2%
b 43310
 
7.4%
a 41511
 
7.1%
y 38449
 
6.6%
t 35545
 
6.1%
c 27734
 
4.7%
m 24082
 
4.1%
l 23750
 
4.1%
Other values (8) 111850
19.1%
Uppercase Letter
ValueCountFrequency (%)
J 29529
24.7%
A 24966
20.9%
M 21585
18.1%
O 11160
 
9.3%
S 10508
 
8.8%
F 8068
 
6.8%
N 6794
 
5.7%
D 6780
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 704781
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 704781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

arrival_date_week_number
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.165173
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:20:57.881599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.605138
Coefficient of variation (CV)0.50083018
Kurtosis-0.98607718
Mean27.165173
Median Absolute Deviation (MAD)11
Skewness-0.010014326
Sum3243250
Variance185.09979
MonotonicityNot monotonic
2023-10-22T16:20:58.110708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 3580
 
3.0%
30 3087
 
2.6%
32 3045
 
2.6%
34 3040
 
2.5%
18 2926
 
2.5%
21 2854
 
2.4%
28 2853
 
2.4%
17 2805
 
2.3%
20 2785
 
2.3%
29 2763
 
2.3%
Other values (43) 89652
75.1%
ValueCountFrequency (%)
1 1047
0.9%
2 1218
1.0%
3 1319
1.1%
4 1487
1.2%
5 1387
1.2%
6 1508
1.3%
7 2109
1.8%
8 2216
1.9%
9 2117
1.8%
10 2149
1.8%
ValueCountFrequency (%)
53 1816
1.5%
52 1195
1.0%
51 933
0.8%
50 1505
1.3%
49 1782
1.5%
48 1504
1.3%
47 1685
1.4%
46 1574
1.3%
45 1941
1.6%
44 2272
1.9%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.798241
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:20:58.320254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7808295
Coefficient of variation (CV)0.55581058
Kurtosis-1.1871683
Mean15.798241
Median Absolute Deviation (MAD)8
Skewness-0.002000454
Sum1886152
Variance77.102966
MonotonicityNot monotonic
2023-10-22T16:20:58.506224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 4406
 
3.7%
5 4317
 
3.6%
15 4196
 
3.5%
25 4160
 
3.5%
26 4147
 
3.5%
9 4096
 
3.4%
12 4087
 
3.4%
16 4078
 
3.4%
2 4055
 
3.4%
19 4052
 
3.4%
Other values (21) 77796
65.2%
ValueCountFrequency (%)
1 3626
3.0%
2 4055
3.4%
3 3855
3.2%
4 3763
3.2%
5 4317
3.6%
6 3833
3.2%
7 3665
3.1%
8 3921
3.3%
9 4096
3.4%
10 3575
3.0%
ValueCountFrequency (%)
31 2208
1.8%
30 3853
3.2%
29 3580
3.0%
28 3946
3.3%
27 3802
3.2%
26 4147
3.5%
25 4160
3.5%
24 3993
3.3%
23 3616
3.0%
22 3596
3.0%

stays_in_weekend_nights
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92759863
Minimum0
Maximum19
Zeros51998
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:20:58.688176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.99861349
Coefficient of variation (CV)1.0765578
Kurtosis7.1740661
Mean0.92759863
Median Absolute Deviation (MAD)1
Skewness1.3800464
Sum110746
Variance0.99722891
MonotonicityNot monotonic
2023-10-22T16:20:58.848300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 51998
43.6%
2 33308
27.9%
1 30626
25.7%
4 1855
 
1.6%
3 1259
 
1.1%
6 153
 
0.1%
5 79
 
0.1%
8 60
 
0.1%
7 19
 
< 0.1%
9 11
 
< 0.1%
Other values (7) 22
 
< 0.1%
ValueCountFrequency (%)
0 51998
43.6%
1 30626
25.7%
2 33308
27.9%
3 1259
 
1.1%
4 1855
 
1.6%
5 79
 
0.1%
6 153
 
0.1%
7 19
 
< 0.1%
8 60
 
0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 5
 
< 0.1%
10 7
 
< 0.1%
9 11
 
< 0.1%
8 60
0.1%
7 19
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5003015
Minimum0
Maximum50
Zeros7645
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:20:59.028883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9082856
Coefficient of variation (CV)0.76322219
Kurtosis24.284555
Mean2.5003015
Median Absolute Deviation (MAD)1
Skewness2.8622492
Sum298511
Variance3.641554
MonotonicityNot monotonic
2023-10-22T16:20:59.225347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 33684
28.2%
1 30310
25.4%
3 22258
18.6%
5 11077
 
9.3%
4 9563
 
8.0%
0 7645
 
6.4%
6 1499
 
1.3%
10 1036
 
0.9%
7 1029
 
0.9%
8 656
 
0.5%
Other values (25) 633
 
0.5%
ValueCountFrequency (%)
0 7645
 
6.4%
1 30310
25.4%
2 33684
28.2%
3 22258
18.6%
4 9563
 
8.0%
5 11077
 
9.3%
6 1499
 
1.3%
7 1029
 
0.9%
8 656
 
0.5%
9 231
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 5
< 0.1%
26 1
 
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8564034
Minimum0
Maximum55
Zeros403
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:20:59.411892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.579261
Coefficient of variation (CV)0.31203401
Kurtosis1352.1151
Mean1.8564034
Median Absolute Deviation (MAD)0
Skewness18.317805
Sum221636
Variance0.3355433
MonotonicityNot monotonic
2023-10-22T16:20:59.566372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 89680
75.1%
1 23027
 
19.3%
3 6202
 
5.2%
0 403
 
0.3%
4 62
 
0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 403
 
0.3%
1 23027
 
19.3%
2 89680
75.1%
3 6202
 
5.2%
4 62
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 62
0.1%

children
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size932.9 KiB
0.0
110796 
1.0
 
4861
2.0
 
3652
3.0
 
76
10.0
 
1

Length

Max length4
Median length3
Mean length3.0000084
Min length3

Characters and Unicode

Total characters358159
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 110796
92.8%
1.0 4861
 
4.1%
2.0 3652
 
3.1%
3.0 76
 
0.1%
10.0 1
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2023-10-22T16:20:59.743647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:20:59.918937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110796
92.8%
1.0 4861
 
4.1%
2.0 3652
 
3.1%
3.0 76
 
0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 238773
66.7%
Other Punctuation 119386
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 230183
96.4%
1 4862
 
2.0%
2 3652
 
1.5%
3 76
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 119386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 358159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
118473 
1
 
900
2
 
15
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.0000084
Min length1

Characters and Unicode

Total characters119391
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 118473
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Length

2023-10-22T16:21:00.072563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:00.248018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 118473
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119391
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119391
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
BB
92310 
HB
14463 
SC
10650 
Undefined
 
1169
FB
 
798

Length

Max length9
Median length2
Mean length2.0685401
Min length2

Characters and Unicode

Total characters246963
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 92310
77.3%
HB 14463
 
12.1%
SC 10650
 
8.9%
Undefined 1169
 
1.0%
FB 798
 
0.7%

Length

2023-10-22T16:21:00.418082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:00.617993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
bb 92310
77.3%
hb 14463
 
12.1%
sc 10650
 
8.9%
undefined 1169
 
1.0%
fb 798
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237611
96.2%
Lowercase Letter 9352
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 199881
84.1%
H 14463
 
6.1%
S 10650
 
4.5%
C 10650
 
4.5%
U 1169
 
0.5%
F 798
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
n 2338
25.0%
d 2338
25.0%
e 2338
25.0%
f 1169
12.5%
i 1169
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 246963
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

country
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct177
Distinct (%)0.1%
Missing488
Missing (%)0.4%
Memory size932.9 KiB
PRT
48590 
GBR
12129 
FRA
10415 
ESP
8568 
DEU
7287 
Other values (172)
31913 

Length

Max length3
Median length3
Mean length2.9892432
Min length2

Characters and Unicode

Total characters355427
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR

Common Values

ValueCountFrequency (%)
PRT 48590
40.7%
GBR 12129
 
10.2%
FRA 10415
 
8.7%
ESP 8568
 
7.2%
DEU 7287
 
6.1%
ITA 3766
 
3.2%
IRL 3375
 
2.8%
BEL 2342
 
2.0%
BRA 2224
 
1.9%
NLD 2104
 
1.8%
Other values (167) 18102
 
15.2%

Length

2023-10-22T16:21:00.780018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt 48590
40.9%
gbr 12129
 
10.2%
fra 10415
 
8.8%
esp 8568
 
7.2%
deu 7287
 
6.1%
ita 3766
 
3.2%
irl 3375
 
2.8%
bel 2342
 
2.0%
bra 2224
 
1.9%
nld 2104
 
1.8%
Other values (167) 18102
 
15.2%

Most occurring characters

ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 355427
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 355427
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 355427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

market_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Online TA
56477 
Offline TA/TO
24219 
Groups
19811 
Direct
12606 
Corporate
 
5295
Other values (3)
 
982

Length

Max length13
Median length9
Mean length9.0197671
Min length6

Characters and Unicode

Total characters1076870
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 56477
47.3%
Offline TA/TO 24219
20.3%
Groups 19811
 
16.6%
Direct 12606
 
10.6%
Corporate 5295
 
4.4%
Complementary 743
 
0.6%
Aviation 237
 
0.2%
Undefined 2
 
< 0.1%

Length

2023-10-22T16:21:00.953448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:01.153264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
online 56477
28.2%
ta 56477
28.2%
offline 24219
12.1%
ta/to 24219
12.1%
groups 19811
 
9.9%
direct 12606
 
6.3%
corporate 5295
 
2.6%
complementary 743
 
0.4%
aviation 237
 
0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 138157
12.8%
O 104915
9.7%
T 104915
9.7%
e 100087
9.3%
i 93778
8.7%
l 81439
7.6%
A 80933
7.5%
80696
7.5%
f 48440
 
4.5%
r 43750
 
4.1%
Other values (16) 199760
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 642735
59.7%
Uppercase Letter 329220
30.6%
Space Separator 80696
 
7.5%
Other Punctuation 24219
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 138157
21.5%
e 100087
15.6%
i 93778
14.6%
l 81439
12.7%
f 48440
 
7.5%
r 43750
 
6.8%
o 31381
 
4.9%
p 25849
 
4.0%
s 19811
 
3.1%
u 19811
 
3.1%
Other values (7) 40232
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
O 104915
31.9%
T 104915
31.9%
A 80933
24.6%
G 19811
 
6.0%
D 12606
 
3.8%
C 6038
 
1.8%
U 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
80696
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 24219
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 971955
90.3%
Common 104915
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 138157
14.2%
O 104915
10.8%
T 104915
10.8%
e 100087
10.3%
i 93778
9.6%
l 81439
8.4%
A 80933
8.3%
f 48440
 
5.0%
r 43750
 
4.5%
o 31381
 
3.2%
Other values (14) 144160
14.8%
Common
ValueCountFrequency (%)
80696
76.9%
/ 24219
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1076870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 138157
12.8%
O 104915
9.7%
T 104915
9.7%
e 100087
9.3%
i 93778
8.7%
l 81439
7.6%
A 80933
7.5%
80696
7.5%
f 48440
 
4.5%
r 43750
 
4.1%
Other values (16) 199760
18.6%

distribution_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
TA/TO
97870 
Direct
14645 
Corporate
 
6677
GDS
 
193
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3433035
Min length3

Characters and Unicode

Total characters637937
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 97870
82.0%
Direct 14645
 
12.3%
Corporate 6677
 
5.6%
GDS 193
 
0.2%
Undefined 5
 
< 0.1%

Length

2023-10-22T16:21:01.351490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:01.538745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 97870
82.0%
direct 14645
 
12.3%
corporate 6677
 
5.6%
gds 193
 
0.2%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 195740
30.7%
/ 97870
15.3%
O 97870
15.3%
A 97870
15.3%
r 27999
 
4.4%
e 21332
 
3.3%
t 21322
 
3.3%
D 14838
 
2.3%
i 14650
 
2.3%
c 14645
 
2.3%
Other values (10) 33801
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 413386
64.8%
Lowercase Letter 126681
 
19.9%
Other Punctuation 97870
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 27999
22.1%
e 21332
16.8%
t 21322
16.8%
i 14650
11.6%
c 14645
11.6%
o 13354
10.5%
a 6677
 
5.3%
p 6677
 
5.3%
n 10
 
< 0.1%
d 10
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 195740
47.4%
O 97870
23.7%
A 97870
23.7%
D 14838
 
3.6%
C 6677
 
1.6%
G 193
 
< 0.1%
S 193
 
< 0.1%
U 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 97870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 540067
84.7%
Common 97870
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 195740
36.2%
O 97870
18.1%
A 97870
18.1%
r 27999
 
5.2%
e 21332
 
3.9%
t 21322
 
3.9%
D 14838
 
2.7%
i 14650
 
2.7%
c 14645
 
2.7%
o 13354
 
2.5%
Other values (9) 20447
 
3.8%
Common
ValueCountFrequency (%)
/ 97870
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 637937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 195740
30.7%
/ 97870
15.3%
O 97870
15.3%
A 97870
15.3%
r 27999
 
4.4%
e 21332
 
3.3%
t 21322
 
3.3%
D 14838
 
2.3%
i 14650
 
2.3%
c 14645
 
2.3%
Other values (10) 33801
 
5.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
115580 
1
 
3810

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Length

2023-10-22T16:21:01.712151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:01.869398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119390
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 119390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

previous_cancellations
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087117849
Minimum0
Maximum26
Zeros112906
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:01.992077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84433638
Coefficient of variation (CV)9.6918874
Kurtosis674.07369
Mean0.087117849
Median Absolute Deviation (MAD)0
Skewness24.458049
Sum10401
Variance0.71290393
MonotonicityNot monotonic
2023-10-22T16:21:02.152469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 112906
94.6%
1 6051
 
5.1%
2 116
 
0.1%
3 65
 
0.1%
24 48
 
< 0.1%
11 35
 
< 0.1%
4 31
 
< 0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
6 22
 
< 0.1%
Other values (5) 65
 
0.1%
ValueCountFrequency (%)
0 112906
94.6%
1 6051
 
5.1%
2 116
 
0.1%
3 65
 
0.1%
4 31
 
< 0.1%
5 19
 
< 0.1%
6 22
 
< 0.1%
11 35
 
< 0.1%
13 12
 
< 0.1%
14 14
 
< 0.1%
ValueCountFrequency (%)
26 26
< 0.1%
25 25
< 0.1%
24 48
< 0.1%
21 1
 
< 0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
13 12
 
< 0.1%
11 35
< 0.1%
6 22
< 0.1%
5 19
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

SKEWED  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13709691
Minimum0
Maximum72
Zeros115770
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:02.345762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4974368
Coefficient of variation (CV)10.92247
Kurtosis767.24521
Mean0.13709691
Median Absolute Deviation (MAD)0
Skewness23.5398
Sum16368
Variance2.2423171
MonotonicityNot monotonic
2023-10-22T16:21:02.612320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115770
97.0%
1 1542
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 115
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 60
 
0.1%
Other values (63) 422
 
0.4%
ValueCountFrequency (%)
0 115770
97.0%
1 1542
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 115
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 60
 
0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
A
85994 
D
19201 
E
 
6535
F
 
2897
G
 
2094
Other values (5)
 
2669

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Length

2023-10-22T16:21:02.908388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:03.157972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a 85994
72.0%
d 19201
 
16.1%
e 6535
 
5.5%
f 2897
 
2.4%
g 2094
 
1.8%
b 1118
 
0.9%
c 932
 
0.8%
h 601
 
0.5%
p 12
 
< 0.1%
l 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 119390
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 119390
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

assigned_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
A
74053 
D
25322 
E
7806 
F
 
3751
G
 
2553
Other values (7)
 
5905

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Length

2023-10-22T16:21:03.509888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 74053
62.0%
d 25322
 
21.2%
e 7806
 
6.5%
f 3751
 
3.1%
g 2553
 
2.1%
c 2375
 
2.0%
b 2163
 
1.8%
h 712
 
0.6%
i 363
 
0.3%
k 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 119390
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 119390
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

booking_changes
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22112405
Minimum0
Maximum21
Zeros101314
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:03.868706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65230557
Coefficient of variation (CV)2.9499531
Kurtosis79.393605
Mean0.22112405
Median Absolute Deviation (MAD)0
Skewness6.0002701
Sum26400
Variance0.42550256
MonotonicityNot monotonic
2023-10-22T16:21:04.223338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 101314
84.9%
1 12701
 
10.6%
2 3805
 
3.2%
3 927
 
0.8%
4 376
 
0.3%
5 118
 
0.1%
6 63
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
Other values (11) 30
 
< 0.1%
ValueCountFrequency (%)
0 101314
84.9%
1 12701
 
10.6%
2 3805
 
3.2%
3 927
 
0.8%
4 376
 
0.3%
5 118
 
0.1%
6 63
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 3
< 0.1%
14 5
< 0.1%
13 5
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
No Deposit
104641 
Non Refund
14587 
Refundable
 
162

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1193900
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 104641
87.6%
Non Refund 14587
 
12.2%
Refundable 162
 
0.1%

Length

2023-10-22T16:21:04.590258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:04.972077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no 104641
43.9%
deposit 104641
43.9%
non 14587
 
6.1%
refund 14587
 
6.1%
refundable 162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 223869
18.8%
e 119552
10.0%
N 119228
10.0%
119228
10.0%
s 104641
8.8%
i 104641
8.8%
t 104641
8.8%
p 104641
8.8%
D 104641
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 836054
70.0%
Uppercase Letter 238618
 
20.0%
Space Separator 119228
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 223869
26.8%
e 119552
14.3%
s 104641
12.5%
i 104641
12.5%
t 104641
12.5%
p 104641
12.5%
n 29336
 
3.5%
f 14749
 
1.8%
u 14749
 
1.8%
d 14749
 
1.8%
Other values (3) 486
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 119228
50.0%
D 104641
43.9%
R 14749
 
6.2%
Space Separator
ValueCountFrequency (%)
119228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1074672
90.0%
Common 119228
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 223869
20.8%
e 119552
11.1%
N 119228
11.1%
s 104641
9.7%
i 104641
9.7%
t 104641
9.7%
p 104641
9.7%
D 104641
9.7%
n 29336
 
2.7%
R 14749
 
1.4%
Other values (6) 44733
 
4.2%
Common
ValueCountFrequency (%)
119228
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1193900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 223869
18.8%
e 119552
10.0%
N 119228
10.0%
119228
10.0%
s 104641
8.8%
i 104641
8.8%
t 104641
8.8%
p 104641
8.8%
D 104641
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

agent
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct333
Distinct (%)0.3%
Missing16340
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean86.693382
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:05.319607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3229
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)220

Descriptive statistics

Standard deviation110.77455
Coefficient of variation (CV)1.277774
Kurtosis-0.0071795649
Mean86.693382
Median Absolute Deviation (MAD)13
Skewness1.0893856
Sum8933753
Variance12271
MonotonicityNot monotonic
2023-10-22T16:21:05.740707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 31961
26.8%
240 13922
11.7%
1 7191
 
6.0%
14 3640
 
3.0%
7 3539
 
3.0%
6 3290
 
2.8%
250 2870
 
2.4%
241 1721
 
1.4%
28 1666
 
1.4%
8 1514
 
1.3%
Other values (323) 31736
26.6%
(Missing) 16340
13.7%
ValueCountFrequency (%)
1 7191
 
6.0%
2 162
 
0.1%
3 1336
 
1.1%
4 47
 
< 0.1%
5 330
 
0.3%
6 3290
 
2.8%
7 3539
 
3.0%
8 1514
 
1.3%
9 31961
26.8%
10 260
 
0.2%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
0.1%
527 35
< 0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
509 10
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 57
< 0.1%

days_in_waiting_list
Real number (ℝ)

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3211492
Minimum0
Maximum391
Zeros115692
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:06.168857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.594721
Coefficient of variation (CV)7.5801767
Kurtosis186.79307
Mean2.3211492
Median Absolute Deviation (MAD)0
Skewness11.944353
Sum277122
Variance309.5742
MonotonicityNot monotonic
2023-10-22T16:21:06.567544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115692
96.9%
39 227
 
0.2%
58 164
 
0.1%
44 141
 
0.1%
31 127
 
0.1%
35 96
 
0.1%
46 94
 
0.1%
69 89
 
0.1%
63 83
 
0.1%
87 80
 
0.1%
Other values (118) 2597
 
2.2%
ValueCountFrequency (%)
0 115692
96.9%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 59
 
< 0.1%
4 25
 
< 0.1%
5 8
 
< 0.1%
6 16
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
391 45
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
< 0.1%
224 10
 
< 0.1%
223 61
0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Transient
89613 
Transient-Party
25124 
Contract
 
4076
Group
 
577

Length

Max length15
Median length9
Mean length10.209146
Min length5

Characters and Unicode

Total characters1218870
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 89613
75.1%
Transient-Party 25124
 
21.0%
Contract 4076
 
3.4%
Group 577
 
0.5%

Length

2023-10-22T16:21:06.968244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:07.295396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
transient 89613
75.1%
transient-party 25124
 
21.0%
contract 4076
 
3.4%
group 577
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 233550
19.2%
t 148013
12.1%
r 144514
11.9%
a 143937
11.8%
T 114737
9.4%
s 114737
9.4%
i 114737
9.4%
e 114737
9.4%
y 25124
 
2.1%
- 25124
 
2.1%
Other values (7) 39660
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1049232
86.1%
Uppercase Letter 144514
 
11.9%
Dash Punctuation 25124
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 233550
22.3%
t 148013
14.1%
r 144514
13.8%
a 143937
13.7%
s 114737
10.9%
i 114737
10.9%
e 114737
10.9%
y 25124
 
2.4%
o 4653
 
0.4%
c 4076
 
0.4%
Other values (2) 1154
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T 114737
79.4%
P 25124
 
17.4%
C 4076
 
2.8%
G 577
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 25124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1193746
97.9%
Common 25124
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 233550
19.6%
t 148013
12.4%
r 144514
12.1%
a 143937
12.1%
T 114737
9.6%
s 114737
9.6%
i 114737
9.6%
e 114737
9.6%
y 25124
 
2.1%
P 25124
 
2.1%
Other values (6) 14536
 
1.2%
Common
ValueCountFrequency (%)
- 25124
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1218870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 233550
19.2%
t 148013
12.1%
r 144514
11.9%
a 143937
11.8%
T 114737
9.4%
s 114737
9.4%
i 114737
9.4%
e 114737
9.4%
y 25124
 
2.1%
- 25124
 
2.1%
Other values (7) 39660
 
3.3%

adr
Real number (ℝ)

Distinct8879
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.83112
Minimum-6.38
Maximum5400
Zeros1959
Zeros (%)1.6%
Negative1
Negative (%)< 0.1%
Memory size932.9 KiB
2023-10-22T16:21:07.693995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile38.4
Q169.29
median94.575
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.71

Descriptive statistics

Standard deviation50.53579
Coefficient of variation (CV)0.49627059
Kurtosis1013.1899
Mean101.83112
Median Absolute Deviation (MAD)27.825
Skewness10.530214
Sum12157618
Variance2553.8661
MonotonicityNot monotonic
2023-10-22T16:21:08.048139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 3754
 
3.1%
75 2715
 
2.3%
90 2473
 
2.1%
65 2418
 
2.0%
0 1959
 
1.6%
80 1889
 
1.6%
95 1661
 
1.4%
120 1607
 
1.3%
100 1573
 
1.3%
85 1538
 
1.3%
Other values (8869) 97803
81.9%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 1959
1.6%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 15
 
< 0.1%
1.29 1
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
510 1
< 0.1%
508 1
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
111974 
1
 
7383
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2023-10-22T16:21:08.401199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:08.602799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119390
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57136276
Minimum0
Maximum5
Zeros70318
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:08.737052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79279842
Coefficient of variation (CV)1.387557
Kurtosis1.4925648
Mean0.57136276
Median Absolute Deviation (MAD)0
Skewness1.3491894
Sum68215
Variance0.62852934
MonotonicityNot monotonic
2023-10-22T16:21:08.897817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 70318
58.9%
1 33226
27.8%
2 12969
 
10.9%
3 2497
 
2.1%
4 340
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
0 70318
58.9%
1 33226
27.8%
2 12969
 
10.9%
3 2497
 
2.1%
4 340
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
5 40
 
< 0.1%
4 340
 
0.3%
3 2497
 
2.1%
2 12969
 
10.9%
1 33226
27.8%
0 70318
58.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Check-Out
75166 
Canceled
43017 
No-Show
 
1207

Length

Max length9
Median length9
Mean length8.619474
Min length7

Characters and Unicode

Total characters1029079
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 75166
63.0%
Canceled 43017
36.0%
No-Show 1207
 
1.0%

Length

2023-10-22T16:21:09.070866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:09.252308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
check-out 75166
63.0%
canceled 43017
36.0%
no-show 1207
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 161200
15.7%
C 118183
11.5%
c 118183
11.5%
h 76373
7.4%
- 76373
7.4%
u 75166
7.3%
t 75166
7.3%
O 75166
7.3%
k 75166
7.3%
a 43017
 
4.2%
Other values (7) 135086
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 756943
73.6%
Uppercase Letter 195763
 
19.0%
Dash Punctuation 76373
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 161200
21.3%
c 118183
15.6%
h 76373
10.1%
u 75166
9.9%
t 75166
9.9%
k 75166
9.9%
a 43017
 
5.7%
n 43017
 
5.7%
l 43017
 
5.7%
d 43017
 
5.7%
Other values (2) 3621
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 118183
60.4%
O 75166
38.4%
N 1207
 
0.6%
S 1207
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 76373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 952706
92.6%
Common 76373
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 161200
16.9%
C 118183
12.4%
c 118183
12.4%
h 76373
8.0%
u 75166
7.9%
t 75166
7.9%
O 75166
7.9%
k 75166
7.9%
a 43017
 
4.5%
n 43017
 
4.5%
Other values (6) 92069
9.7%
Common
ValueCountFrequency (%)
- 76373
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1029079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 161200
15.7%
C 118183
11.5%
c 118183
11.5%
h 76373
7.4%
- 76373
7.4%
u 75166
7.3%
t 75166
7.3%
O 75166
7.3%
k 75166
7.3%
a 43017
 
4.2%
Other values (7) 135086
13.1%
Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
10/21/2015
 
1461
7/6/2015
 
805
11/25/2016
 
790
1/1/2015
 
763
1/18/2016
 
625
Other values (921)
114946 

Length

Max length10
Median length9
Mean length8.9224893
Min length8

Characters and Unicode

Total characters1065256
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row7/1/2015
2nd row7/1/2015
3rd row7/2/2015
4th row7/2/2015
5th row7/3/2015

Common Values

ValueCountFrequency (%)
10/21/2015 1461
 
1.2%
7/6/2015 805
 
0.7%
11/25/2016 790
 
0.7%
1/1/2015 763
 
0.6%
1/18/2016 625
 
0.5%
7/2/2015 469
 
0.4%
12/7/2016 450
 
0.4%
12/18/2015 423
 
0.4%
2/9/2016 412
 
0.3%
4/4/2016 382
 
0.3%
Other values (916) 112810
94.5%

Length

2023-10-22T16:21:09.417265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10/21/2015 1461
 
1.2%
7/6/2015 805
 
0.7%
11/25/2016 790
 
0.7%
1/1/2015 763
 
0.6%
1/18/2016 625
 
0.5%
7/2/2015 469
 
0.4%
12/7/2016 450
 
0.4%
12/18/2015 423
 
0.4%
2/9/2016 412
 
0.3%
4/4/2016 382
 
0.3%
Other values (916) 112810
94.5%

Most occurring characters

ValueCountFrequency (%)
/ 238780
22.4%
1 218578
20.5%
2 187927
17.6%
0 141411
13.3%
6 79165
 
7.4%
7 60096
 
5.6%
5 46838
 
4.4%
3 26867
 
2.5%
8 23119
 
2.2%
9 21359
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 826476
77.6%
Other Punctuation 238780
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 218578
26.4%
2 187927
22.7%
0 141411
17.1%
6 79165
 
9.6%
7 60096
 
7.3%
5 46838
 
5.7%
3 26867
 
3.3%
8 23119
 
2.8%
9 21359
 
2.6%
4 21116
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 238780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1065256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 238780
22.4%
1 218578
20.5%
2 187927
17.6%
0 141411
13.3%
6 79165
 
7.4%
7 60096
 
5.6%
5 46838
 
4.4%
3 26867
 
2.5%
8 23119
 
2.2%
9 21359
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1065256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 238780
22.4%
1 218578
20.5%
2 187927
17.6%
0 141411
13.3%
6 79165
 
7.4%
7 60096
 
5.6%
5 46838
 
4.4%
3 26867
 
2.5%
8 23119
 
2.2%
9 21359
 
2.0%

MO_YR
Categorical

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
10-2015
 
5742
3-2016
 
5319
1-2017
 
5251
10-2016
 
5221
4-2016
 
5214
Other values (30)
92643 

Length

Max length7
Median length6
Mean length6.223151
Min length6

Characters and Unicode

Total characters742982
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row7-2015
2nd row7-2015
3rd row7-2015
4th row7-2015
5th row7-2015

Common Values

ValueCountFrequency (%)
10-2015 5742
 
4.8%
3-2016 5319
 
4.5%
1-2017 5251
 
4.4%
10-2016 5221
 
4.4%
4-2016 5214
 
4.4%
5-2016 5023
 
4.2%
11-2016 5021
 
4.2%
5-2017 5006
 
4.2%
9-2016 4993
 
4.2%
2-2017 4858
 
4.1%
Other values (25) 67742
56.7%

Length

2023-10-22T16:21:09.596763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10-2015 5742
 
4.8%
3-2016 5319
 
4.5%
1-2017 5251
 
4.4%
10-2016 5221
 
4.4%
4-2016 5214
 
4.4%
5-2016 5023
 
4.2%
11-2016 5021
 
4.2%
5-2017 5006
 
4.2%
9-2016 4993
 
4.2%
2-2017 4858
 
4.1%
Other values (25) 67742
56.7%

Most occurring characters

ValueCountFrequency (%)
1 164812
22.2%
2 136288
18.3%
0 130533
17.6%
- 119390
16.1%
6 67075
9.0%
7 48589
 
6.5%
5 35233
 
4.7%
8 11249
 
1.5%
3 10230
 
1.4%
4 10180
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 623592
83.9%
Dash Punctuation 119390
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 164812
26.4%
2 136288
21.9%
0 130533
20.9%
6 67075
10.8%
7 48589
 
7.8%
5 35233
 
5.7%
8 11249
 
1.8%
3 10230
 
1.6%
4 10180
 
1.6%
9 9403
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 119390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 742982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 164812
22.2%
2 136288
18.3%
0 130533
17.6%
- 119390
16.1%
6 67075
9.0%
7 48589
 
6.5%
5 35233
 
4.7%
8 11249
 
1.5%
3 10230
 
1.4%
4 10180
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 742982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 164812
22.2%
2 136288
18.3%
0 130533
17.6%
- 119390
16.1%
6 67075
9.0%
7 48589
 
6.5%
5 35233
 
4.7%
8 11249
 
1.5%
3 10230
 
1.4%
4 10180
 
1.4%

CPI_AVG
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean240.78065
Minimum234.747
Maximum246.435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:09.768535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum234.747
5-th percentile237.336
Q1238.033
median240.545
Q3243.892
95-th percentile244.243
Maximum246.435
Range11.688
Interquartile range (IQR)5.859

Descriptive statistics

Standard deviation2.691831
Coefficient of variation (CV)0.011179598
Kurtosis-1.3880182
Mean240.78065
Median Absolute Deviation (MAD)2.784
Skewness0.07235439
Sum28703221
Variance7.2459541
MonotonicityNot monotonic
2023-10-22T16:21:09.976774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
237.733 5742
 
4.8%
238.08 5319
 
4.5%
243.618 5251
 
4.4%
241.741 5221
 
4.4%
238.992 5214
 
4.4%
239.557 5023
 
4.2%
242.026 5021
 
4.2%
244.004 5006
 
4.2%
241.176 4993
 
4.2%
244.006 4858
 
4.1%
Other values (23) 67561
56.6%
ValueCountFrequency (%)
234.747 948
 
0.8%
235.342 44
 
< 0.1%
235.976 85
 
0.1%
236.222 151
 
0.1%
237.001 275
 
0.2%
237.336 4596
3.8%
237.498 4017
3.4%
237.652 4482
3.8%
237.657 666
 
0.6%
237.733 5742
4.8%
ValueCountFrequency (%)
246.435 393
 
0.3%
245.183 3417
2.9%
244.243 4038
3.4%
244.193 4634
3.9%
244.163 4060
3.4%
244.006 4858
4.1%
244.004 5006
4.2%
243.892 4826
4.0%
243.618 5251
4.4%
242.637 4338
3.6%

INFLATION
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.0439883
Minimum1.6
Maximum2.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:10.146066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile1.7
Q11.9
median2.1
Q32.2
95-th percentile2.3
Maximum2.3
Range0.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.20338831
Coefficient of variation (CV)0.099505616
Kurtosis-1.0559743
Mean2.0439883
Median Absolute Deviation (MAD)0.1
Skewness-0.54942414
Sum243661.8
Variance0.041366806
MonotonicityNot monotonic
2023-10-22T16:21:10.291610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.2 38018
31.8%
2.1 18518
15.5%
1.7 17233
14.4%
2.3 14432
 
12.1%
1.9 14393
 
12.1%
2 7903
 
6.6%
1.8 7764
 
6.5%
1.6 948
 
0.8%
(Missing) 181
 
0.2%
ValueCountFrequency (%)
1.6 948
 
0.8%
1.7 17233
14.4%
1.8 7764
 
6.5%
1.9 14393
 
12.1%
2 7903
 
6.6%
2.1 18518
15.5%
2.2 38018
31.8%
2.3 14432
 
12.1%
ValueCountFrequency (%)
2.3 14432
 
12.1%
2.2 38018
31.8%
2.1 18518
15.5%
2 7903
 
6.6%
1.9 14393
 
12.1%
1.8 7764
 
6.5%
1.7 17233
14.4%
1.6 948
 
0.8%

INFLATION_CHG
Categorical

Distinct4
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Memory size932.9 KiB
0.1
40174 
0.0
38689 
-0.1
30514 
-0.2
9832 

Length

Max length4
Median length3
Mean length3.3384476
Min length3

Characters and Unicode

Total characters397973
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.1 40174
33.6%
0.0 38689
32.4%
-0.1 30514
25.6%
-0.2 9832
 
8.2%
(Missing) 181
 
0.2%

Length

2023-10-22T16:21:10.487243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:10.663318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.1 70688
59.3%
0.0 38689
32.5%
0.2 9832
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 157898
39.7%
. 119209
30.0%
1 70688
17.8%
- 40346
 
10.1%
2 9832
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 238418
59.9%
Other Punctuation 119209
30.0%
Dash Punctuation 40346
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157898
66.2%
1 70688
29.6%
2 9832
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 119209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 397973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157898
39.7%
. 119209
30.0%
1 70688
17.8%
- 40346
 
10.1%
2 9832
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 397973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157898
39.7%
. 119209
30.0%
1 70688
17.8%
- 40346
 
10.1%
2 9832
 
2.5%

CSMR_SENT
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean93.093092
Minimum87.2
Maximum98.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:10.836260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum87.2
5-th percentile87.2
Q191
median93.1
Q396.3
95-th percentile98.2
Maximum98.5
Range11.3
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation3.272362
Coefficient of variation (CV)0.035151501
Kurtosis-1.0209587
Mean93.093092
Median Absolute Deviation (MAD)3.1
Skewness-0.0095403689
Sum11097534
Variance10.708353
MonotonicityNot monotonic
2023-10-22T16:21:11.029664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
90 10195
 
8.5%
87.2 9238
 
7.7%
91 5319
 
4.5%
98.5 5251
 
4.4%
89 5214
 
4.4%
94.7 5023
 
4.2%
93.8 5021
 
4.2%
97.1 5006
 
4.2%
91.2 4993
 
4.2%
96.3 4858
 
4.1%
Other values (21) 59091
49.5%
ValueCountFrequency (%)
87.2 9238
7.7%
89 5214
4.4%
89.8 4585
3.8%
90 10195
8.5%
90.7 275
 
0.2%
91 5319
4.5%
91.2 4993
4.2%
91.3 3077
 
2.6%
91.7 4596
3.8%
91.9 3247
 
2.7%
ValueCountFrequency (%)
98.5 5251
4.4%
98.2 4338
3.6%
98.1 948
 
0.8%
97.1 5006
4.2%
97 4634
3.9%
96.9 4826
4.0%
96.8 3417
2.9%
96.3 4858
4.1%
96.1 666
 
0.6%
95.9 151
 
0.1%

UNRATE
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.8279677
Minimum4.3
Maximum5.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:11.204627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile4.3
Q14.7
median4.9
Q35
95-th percentile5.2
Maximum5.7
Range1.4
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.279105
Coefficient of variation (CV)0.057810039
Kurtosis-0.15225667
Mean4.8279677
Median Absolute Deviation (MAD)0.2
Skewness-0.19018045
Sum575537.2
Variance0.077899602
MonotonicityNot monotonic
2023-10-22T16:21:11.351242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5 23736
19.9%
4.9 19786
16.6%
4.7 14447
12.1%
4.4 14093
11.8%
4.8 13733
11.5%
5.1 12102
10.1%
4.3 7455
 
6.2%
4.6 4826
 
4.0%
5.3 3615
 
3.0%
5.2 3247
 
2.7%
Other values (4) 2169
 
1.8%
ValueCountFrequency (%)
4.3 7455
 
6.2%
4.4 14093
11.8%
4.6 4826
 
4.0%
4.7 14447
12.1%
4.8 13733
11.5%
4.9 19786
16.6%
5 23736
19.9%
5.1 12102
10.1%
5.2 3247
 
2.7%
5.3 3615
 
3.0%
ValueCountFrequency (%)
5.7 44
 
< 0.1%
5.6 1614
 
1.4%
5.5 85
 
0.1%
5.4 426
 
0.4%
5.3 3615
 
3.0%
5.2 3247
 
2.7%
5.1 12102
10.1%
5 23736
19.9%
4.9 19786
16.6%
4.8 13733
11.5%

INTRSRT
Categorical

Distinct5
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Memory size932.9 KiB
1.0
56521 
0.75
21867 
1.5
14466 
1.25
14447 
1.75
11908 

Length

Max length4
Median length3
Mean length3.4045164
Min length3

Characters and Unicode

Total characters405849
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.75
2nd row0.75
3rd row0.75
4th row0.75
5th row0.75

Common Values

ValueCountFrequency (%)
1.0 56521
47.3%
0.75 21867
 
18.3%
1.5 14466
 
12.1%
1.25 14447
 
12.1%
1.75 11908
 
10.0%
(Missing) 181
 
0.2%

Length

2023-10-22T16:21:11.528513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:11.727264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 56521
47.4%
0.75 21867
 
18.3%
1.5 14466
 
12.1%
1.25 14447
 
12.1%
1.75 11908
 
10.0%

Most occurring characters

ValueCountFrequency (%)
. 119209
29.4%
1 97342
24.0%
0 78388
19.3%
5 62688
15.4%
7 33775
 
8.3%
2 14447
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 286640
70.6%
Other Punctuation 119209
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 97342
34.0%
0 78388
27.3%
5 62688
21.9%
7 33775
 
11.8%
2 14447
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 119209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 405849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 119209
29.4%
1 97342
24.0%
0 78388
19.3%
5 62688
15.4%
7 33775
 
8.3%
2 14447
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 405849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 119209
29.4%
1 97342
24.0%
0 78388
19.3%
5 62688
15.4%
7 33775
 
8.3%
2 14447
 
3.6%

GDP
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18796.313
Minimum17991.348
Maximum19561.896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:11.895276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17991.348
5-th percentile18306.96
Q118425.306
median18775.459
Q319148.194
95-th percentile19561.896
Maximum19561.896
Range1570.548
Interquartile range (IQR)722.888

Descriptive statistics

Standard deviation401.00869
Coefficient of variation (CV)0.021334434
Kurtosis-1.081704
Mean18796.313
Median Absolute Deviation (MAD)372.735
Skewness0.25386092
Sum2.2406897 × 109
Variance160807.97
MonotonicityNot monotonic
2023-10-22T16:21:12.068780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19148.194 14935
12.5%
18611.617 14789
12.4%
18968.041 14580
12.2%
18425.306 14397
12.1%
18775.459 14031
11.8%
19304.506 13700
11.5%
18332.079 11881
10.0%
18306.96 10879
9.1%
19561.896 7848
6.6%
18193.707 1092
 
0.9%
ValueCountFrequency (%)
17991.348 1077
 
0.9%
18193.707 1092
 
0.9%
18306.96 10879
9.1%
18332.079 11881
10.0%
18425.306 14397
12.1%
18611.617 14789
12.4%
18775.459 14031
11.8%
18968.041 14580
12.2%
19148.194 14935
12.5%
19304.506 13700
11.5%
ValueCountFrequency (%)
19561.896 7848
6.6%
19304.506 13700
11.5%
19148.194 14935
12.5%
18968.041 14580
12.2%
18775.459 14031
11.8%
18611.617 14789
12.4%
18425.306 14397
12.1%
18332.079 11881
10.0%
18306.96 10879
9.1%
18193.707 1092
 
0.9%

FUEL_PRCS
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean157.64872
Minimum113.4
Maximum204.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:12.257780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum113.4
5-th percentile119.2
Q1149.8
median161.1
Q3171.5
95-th percentile189.2
Maximum204.2
Range90.8
Interquartile range (IQR)21.7

Descriptive statistics

Standard deviation21.373932
Coefficient of variation (CV)0.13557948
Kurtosis-0.27716217
Mean157.64872
Median Absolute Deviation (MAD)11.3
Skewness-0.52151258
Sum18793147
Variance456.84499
MonotonicityNot monotonic
2023-10-22T16:21:12.447953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
173.5 5742
 
4.8%
119.4 5319
 
4.5%
161.1 5251
 
4.4%
159.7 5221
 
4.4%
123.6 5214
 
4.4%
144.4 5023
 
4.2%
157 5021
 
4.2%
173.6 5006
 
4.2%
163.1 4993
 
4.2%
163.5 4858
 
4.1%
Other values (23) 67561
56.6%
ValueCountFrequency (%)
113.4 4596
3.8%
119.2 4482
3.8%
119.4 5319
4.5%
123.6 5214
4.4%
130.8 3062
2.6%
144.4 5023
4.2%
149.8 4585
3.8%
155.4 4552
3.8%
157 5021
4.2%
157.6 4453
3.7%
ValueCountFrequency (%)
204.2 393
 
0.3%
202.6 275
 
0.2%
198.7 666
 
0.6%
194 3615
3.0%
193.1 85
 
0.1%
191.5 44
 
< 0.1%
189.2 3247
2.7%
188.9 3417
2.9%
183.8 151
 
0.1%
182.6 948
 
0.8%

CPI_HOTELS
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.17732386
Minimum0.10705869
Maximum0.23650356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:12.648386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.10705869
5-th percentile0.13233874
Q10.16567662
median0.1835467
Q30.18982146
95-th percentile0.20797522
Maximum0.23650356
Range0.12944488
Interquartile range (IQR)0.024144836

Descriptive statistics

Standard deviation0.023983805
Coefficient of variation (CV)0.13525425
Kurtosis1.3256709
Mean0.17732386
Median Absolute Deviation (MAD)0.013772336
Skewness-0.66519423
Sum21138.599
Variance0.00057522291
MonotonicityNot monotonic
2023-10-22T16:21:12.852976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.189821457 5742
 
4.8%
0.185238802 5319
 
4.5%
0.165676621 5251
 
4.4%
0.189165278 5221
 
4.4%
0.176264825 5214
 
4.4%
0.198446697 5023
 
4.2%
0.163833402 5021
 
4.2%
0.163110904 5006
 
4.2%
0.187796703 4993
 
4.2%
0.165753625 4858
 
4.1%
Other values (23) 67561
56.6%
ValueCountFrequency (%)
0.107058689 4038
3.4%
0.132338735 4060
3.4%
0.138513378 3417
2.9%
0.150882526 4826
4.0%
0.151972037 393
 
0.3%
0.163110904 5006
4.2%
0.163833402 5021
4.2%
0.165676621 5251
4.4%
0.165753625 4858
4.1%
0.169333153 3062
2.6%
ValueCountFrequency (%)
0.236503564 948
 
0.8%
0.23007677 44
 
< 0.1%
0.226075176 4552
3.8%
0.218614167 85
 
0.1%
0.216699359 151
 
0.1%
0.207975217 4585
3.8%
0.198446697 5023
4.2%
0.197319035 4596
3.8%
0.18998938 4482
3.8%
0.189821457 5742
4.8%

US_GINI
Categorical

Distinct2
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Memory size932.9 KiB
41.2
61412 
41.1
57797 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters476836
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41.2
2nd row41.2
3rd row41.2
4th row41.2
5th row41.2

Common Values

ValueCountFrequency (%)
41.2 61412
51.4%
41.1 57797
48.4%
(Missing) 181
 
0.2%

Length

2023-10-22T16:21:13.087077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:21:13.249856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
41.2 61412
51.5%
41.1 57797
48.5%

Most occurring characters

ValueCountFrequency (%)
1 177006
37.1%
4 119209
25.0%
. 119209
25.0%
2 61412
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 357627
75.0%
Other Punctuation 119209
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 177006
49.5%
4 119209
33.3%
2 61412
 
17.2%
Other Punctuation
ValueCountFrequency (%)
. 119209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 476836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 177006
37.1%
4 119209
25.0%
. 119209
25.0%
2 61412
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 476836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 177006
37.1%
4 119209
25.0%
. 119209
25.0%
2 61412
 
12.9%

DIS_INC
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing181
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41970.477
Minimum41182
Maximum42834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2023-10-22T16:21:13.398220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum41182
5-th percentile41420
Q141680
median41850
Q342274
95-th percentile42806
Maximum42834
Range1652
Interquartile range (IQR)594

Descriptive statistics

Standard deviation426.73988
Coefficient of variation (CV)0.01016762
Kurtosis-0.57788585
Mean41970.477
Median Absolute Deviation (MAD)178
Skewness0.66277146
Sum5.0032586 × 109
Variance182106.93
MonotonicityNot monotonic
2023-10-22T16:21:13.588109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
41560 5742
 
4.8%
41829 5319
 
4.5%
42182 5251
 
4.4%
41884 5221
 
4.4%
41727 5214
 
4.4%
41680 5023
 
4.2%
41952 5021
 
4.2%
42742 5006
 
4.2%
41852 4993
 
4.2%
42274 4858
 
4.1%
Other values (23) 67561
56.6%
ValueCountFrequency (%)
41182 85
 
0.1%
41199 948
 
0.8%
41248 151
 
0.1%
41288 666
 
0.6%
41290 275
 
0.2%
41324 44
 
< 0.1%
41355 3615
3.0%
41420 3247
2.7%
41504 4017
3.4%
41526 3077
2.6%
ValueCountFrequency (%)
42834 393
 
0.3%
42809 4038
3.4%
42806 3417
2.9%
42742 5006
4.2%
42724 4060
3.4%
42481 4634
3.9%
42428 4826
4.0%
42274 4858
4.1%
42182 5251
4.4%
42013 4338
3.6%

Interactions

2023-10-22T16:20:44.362841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:18:42.800238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:18:48.578876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:18:55.675074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:04.362705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:09.680181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:16.826498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:21.974696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:26.964356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:34.051865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:39.292715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-10-22T16:19:56.381019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:02.805106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:08.966983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:14.032097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:21.654493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:26.625253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:32.347086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:38.866480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:43.898601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:51.327257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:18:48.135659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:18:54.969037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:04.134284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:09.418514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:16.567852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:21.745200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:26.641868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:33.810822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:39.057199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:44.623352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:51.254064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:19:56.613799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:03.204262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:09.216959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:14.262313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:21.887470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:26.869032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:32.744557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:39.092133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-22T16:20:44.128953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-10-22T16:21:13.832160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
lead_timearrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultsprevious_cancellationsprevious_bookings_not_canceledbooking_changesagentdays_in_waiting_listadrtotal_of_special_requestsCPI_AVGINFLATIONCSMR_SENTUNRATEGDPFUEL_PRCSCPI_HOTELSDIS_INChotelis_canceledarrival_date_yeararrival_date_monthchildrenbabiesmealmarket_segmentdistribution_channelis_repeated_guestreserved_room_typeassigned_room_typedeposit_typecustomer_typerequired_car_parking_spacesreservation_statusMO_YRINFLATION_CHGINTRSRTUS_GINI
lead_time1.0000.1130.0080.1620.2960.1920.171-0.189-0.008-0.1230.1530.015-0.0740.005-0.100-0.0180.031-0.0070.1080.051-0.0400.0940.2810.1040.1320.0280.0070.0890.1700.1160.1340.0480.0620.2730.1220.0570.2070.1740.0920.1200.083
arrival_date_week_number0.1131.0000.0610.0260.0260.0260.087-0.0430.008-0.057-0.0040.0740.019-0.158-0.208-0.3230.307-0.2250.2440.144-0.2860.0670.0660.4240.8010.0620.0140.0800.0810.0640.0750.0420.0280.0950.1060.0170.0610.5790.4400.4290.142
arrival_date_day_of_month0.0080.0611.000-0.007-0.0160.002-0.012-0.0010.0120.0050.0320.0270.0030.0150.002-0.001-0.0090.020-0.008-0.0070.0190.0260.0210.0440.0580.0100.0050.0390.0330.0280.0170.0100.0090.0540.0320.0080.0230.0690.0250.0360.025
stays_in_weekend_nights0.1620.026-0.0071.0000.2380.127-0.055-0.0840.0400.131-0.0750.0510.0790.054-0.0190.008-0.0380.0460.031-0.0340.0320.1980.0220.0290.0460.0280.0100.0610.0610.0550.0820.0540.0510.0730.0880.0150.0240.0420.0260.0270.021
stays_in_week_nights0.2960.026-0.0160.2381.0000.153-0.062-0.1190.0640.1700.0120.0940.0760.048-0.0160.006-0.0370.0440.032-0.0260.0290.1920.0280.0140.0370.0130.0000.0450.0330.0060.0170.0440.0470.0470.0800.0170.0300.0290.0250.0230.007
adults0.1920.0260.0020.1270.1531.000-0.036-0.210-0.085-0.056-0.0370.2800.1620.057-0.0310.006-0.0420.0470.043-0.0130.0270.0140.0130.0150.0100.0000.0000.0000.0080.0080.0000.0030.0000.0000.0890.0000.0080.0490.0060.0090.008
previous_cancellations0.1710.087-0.012-0.055-0.062-0.0361.0000.102-0.073-0.1680.116-0.150-0.129-0.246-0.1820.0130.288-0.2950.1680.088-0.2810.0500.0440.0520.0320.0000.0000.0880.0540.0510.1850.0060.0080.0510.0090.0000.0310.2260.0260.0370.035
previous_bookings_not_canceled-0.189-0.043-0.001-0.084-0.119-0.2100.1021.0000.0310.060-0.019-0.1430.0250.0390.0210.039-0.0440.045-0.029-0.0380.0520.0170.0410.0250.0170.0020.0000.0140.0970.1080.3200.0030.0030.0130.0140.0190.0290.0200.0150.0260.018
booking_changes-0.0080.0080.0120.0400.064-0.085-0.0730.0311.0000.091-0.0190.0050.0420.068-0.0130.004-0.0530.0670.011-0.0360.0560.0400.0480.0160.0100.0180.0170.0110.0200.0270.0000.0140.0510.0290.0280.0160.0340.0190.0020.0190.002
agent-0.123-0.0570.0050.1310.170-0.056-0.1680.0600.0911.000-0.019-0.0490.0150.1090.0500.053-0.1110.125-0.075-0.0960.1300.8170.0860.0910.0830.0580.0260.1850.2220.2090.0760.1430.1330.1190.1250.1310.0640.1060.0730.0840.071
days_in_waiting_list0.153-0.0040.032-0.0750.012-0.0370.116-0.019-0.019-0.0191.000-0.040-0.123-0.1500.037-0.0720.116-0.130-0.0880.086-0.0990.0870.0680.0740.0600.0180.0000.0620.0780.0270.0240.0280.0290.1270.0780.0340.0500.1130.0560.0590.076
adr0.0150.0740.0270.0510.0940.280-0.150-0.1430.005-0.049-0.0401.0000.1960.291-0.1070.049-0.2530.2850.142-0.1250.2140.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.000
total_of_special_requests-0.0740.0190.0030.0790.0760.162-0.1290.0250.0420.015-0.1230.1961.0000.200-0.0480.053-0.1750.2020.067-0.1200.1780.0460.2650.0910.0530.0610.0600.0620.2100.0700.0400.0750.0660.2200.0970.0440.1890.1220.0600.0990.040
CPI_AVG0.005-0.1580.0150.0540.0480.057-0.2460.0390.0680.109-0.1500.2910.2001.000-0.2240.590-0.8200.9580.332-0.7080.8630.0560.2300.7880.3690.0530.0060.0940.1230.0380.0480.0590.0470.2240.2220.0240.1641.0000.6100.7080.837
INFLATION-0.100-0.2080.002-0.019-0.016-0.031-0.1820.021-0.0130.0500.037-0.107-0.048-0.2241.000-0.1220.070-0.137-0.7570.492-0.1280.0610.2000.7030.3780.0410.0090.0890.1130.0470.0500.0520.0420.1700.2340.0170.1441.0000.5760.7000.812
CSMR_SENT-0.018-0.323-0.0010.0080.0060.0060.0130.0390.0040.053-0.0720.0490.0530.590-0.1221.000-0.5730.5570.247-0.5450.5590.0630.1960.6160.4070.0310.0080.0800.0740.0290.0440.0440.0380.1640.0890.0150.1431.0000.6790.6390.563
UNRATE0.0310.307-0.009-0.038-0.037-0.0420.288-0.044-0.053-0.1110.116-0.253-0.175-0.8200.070-0.5731.000-0.878-0.1530.586-0.8900.0370.2460.7790.3540.0420.0140.1090.1160.0450.0590.0520.0490.1770.1880.0210.1781.0000.5190.7190.743
GDP-0.007-0.2250.0200.0460.0440.047-0.2950.0450.0670.125-0.1300.2850.2020.958-0.1370.557-0.8781.0000.190-0.6630.9400.0570.2490.7880.4000.0600.0090.0860.1260.0460.0530.0640.0490.2140.2120.0260.1791.0000.4320.7510.839
FUEL_PRCS0.1080.244-0.0080.0310.0320.0430.168-0.0290.011-0.075-0.0880.1420.0670.332-0.7570.247-0.1530.1901.000-0.4380.1290.0590.1230.6010.4030.0420.0100.0820.0660.0350.0410.0340.0320.1040.1330.0140.0901.0000.5610.5720.836
CPI_HOTELS0.0510.144-0.007-0.034-0.026-0.0130.088-0.038-0.036-0.0960.086-0.125-0.120-0.7080.492-0.5450.586-0.663-0.4381.000-0.7130.0590.1710.6190.4050.0490.0060.0870.0770.0400.0440.0410.0320.1260.1110.0180.1221.0000.5640.6800.622
DIS_INC-0.040-0.2860.0190.0320.0290.027-0.2810.0520.0560.130-0.0990.2140.1780.863-0.1280.559-0.8900.9400.129-0.7131.0000.0270.2370.8590.4150.0390.0070.1020.1120.0460.0500.0560.0480.1770.1880.0230.1711.0000.5600.9040.898
hotel0.0940.0670.0260.1980.1920.0140.0500.0170.0400.8170.0870.0000.0460.0560.0610.0630.0370.0570.0590.0590.0271.0000.1360.0430.0700.0460.0490.3170.1470.1870.0500.3230.3910.1770.0520.2210.1360.0990.0160.0090.015
is_canceled0.2810.0660.0210.0220.0280.0130.0440.0410.0480.0860.0680.0000.2650.2300.2000.1960.2460.2490.1230.1710.2370.1361.0000.0260.0700.0280.0340.0500.2670.1770.0850.0730.2030.4810.1360.1971.0000.2960.1010.1820.000
arrival_date_year0.1040.4240.0440.0290.0140.0150.0520.0250.0160.0910.0740.0000.0910.7880.7030.6160.7790.7880.6010.6190.8590.0430.0261.0000.4290.0440.0090.1120.1590.0270.0100.0820.0530.0520.2130.0180.0230.9120.3810.8730.860
arrival_date_month0.1320.8010.0580.0460.0370.0100.0320.0170.0100.0830.0600.0010.0530.3690.3780.4070.3540.4000.4030.4050.4150.0700.0700.4291.0000.0690.0160.0890.0880.0690.0750.0450.0270.1010.1030.0180.0650.6410.4920.4450.145
children0.0280.0620.0100.0280.0130.0000.0000.0020.0180.0580.0180.0000.0610.0530.0410.0310.0420.0600.0420.0490.0390.0460.0280.0440.0691.0000.0250.0370.1000.0430.0350.3570.3040.0730.0610.0300.0280.0720.0190.0440.017
babies0.0070.0140.0050.0100.0000.0000.0000.0000.0170.0260.0000.0000.0600.0060.0090.0080.0140.0090.0100.0060.0070.0490.0340.0090.0160.0251.0000.0150.0340.0290.0070.0400.0440.0230.0150.0200.0240.0160.0050.0040.000
meal0.0890.0800.0390.0610.0450.0000.0880.0140.0110.1850.0620.0000.0620.0940.0890.0800.1090.0860.0820.0870.1020.3170.0500.1120.0890.0370.0151.0000.1910.0770.0600.1030.1160.0930.1390.0270.0400.1400.0660.0920.049
market_segment0.1700.0810.0330.0610.0330.0080.0540.0970.0200.2220.0780.0000.2100.1230.1130.0740.1160.1260.0660.0770.1120.1470.2670.1590.0880.1000.0340.1911.0000.6920.3470.1380.1210.3740.2760.0920.1950.1570.0650.1500.104
distribution_channel0.1160.0640.0280.0550.0060.0080.0510.1080.0270.2090.0270.0000.0700.0380.0470.0290.0450.0460.0350.0400.0460.1870.1770.0270.0690.0430.0290.0770.6921.0000.2970.1000.0950.0910.0790.0760.1290.0690.0170.0280.015
is_repeated_guest0.1340.0750.0170.0820.0170.0000.1850.3200.0000.0760.0240.0000.0400.0480.0500.0440.0590.0530.0410.0440.0500.0500.0850.0100.0750.0350.0070.0600.3470.2971.0000.0370.0710.0580.1050.0780.0860.2250.0300.0460.003
reserved_room_type0.0480.0420.0100.0540.0440.0030.0060.0030.0140.1430.0280.0000.0750.0590.0520.0440.0520.0640.0340.0410.0560.3230.0730.0820.0450.3570.0400.1030.1380.1000.0371.0000.7760.1520.1090.0790.0520.0690.0500.0790.050
assigned_room_type0.0620.0280.0090.0510.0470.0000.0080.0030.0510.1330.0290.0000.0660.0470.0420.0380.0490.0490.0320.0320.0480.3910.2030.0530.0270.3040.0440.1160.1210.0950.0710.7761.0000.1920.0900.0920.1450.0530.0380.0600.053
deposit_type0.2730.0950.0540.0730.0470.0000.0510.0130.0290.1190.1270.0070.2200.2240.1700.1640.1770.2140.1040.1260.1770.1770.4810.0520.1010.0730.0230.0930.3740.0910.0580.1520.1921.0000.0980.0710.3470.2600.1090.1700.059
customer_type0.1220.1060.0320.0880.0800.0890.0090.0140.0280.1250.0780.0000.0970.2220.2340.0890.1880.2120.1330.1110.1880.0520.1360.2130.1030.0610.0150.1390.2760.0790.1050.1090.0900.0981.0000.0410.0970.2660.0510.1590.126
required_car_parking_spaces0.0570.0170.0080.0150.0170.0000.0000.0190.0160.1310.0340.0000.0440.0240.0170.0150.0210.0260.0140.0180.0230.2210.1970.0180.0180.0300.0200.0270.0920.0760.0780.0790.0920.0710.0411.0000.1390.0300.0110.0180.019
reservation_status0.2070.0610.0230.0240.0300.0080.0310.0290.0340.0640.0500.0000.1890.1640.1440.1430.1780.1790.0900.1220.1710.1361.0000.0230.0650.0280.0240.0400.1950.1290.0860.0520.1450.3470.0970.1391.0000.2140.0720.1310.013
MO_YR0.1740.5790.0690.0420.0290.0490.2260.0200.0190.1060.1130.0000.1221.0001.0001.0001.0001.0001.0001.0001.0000.0990.2960.9120.6410.0720.0160.1400.1570.0690.2250.0690.0530.2600.2660.0300.2141.0001.0001.0001.000
INFLATION_CHG0.0920.4400.0250.0260.0250.0060.0260.0150.0020.0730.0560.0000.0600.6100.5760.6790.5190.4320.5610.5640.5600.0160.1010.3810.4920.0190.0050.0660.0650.0170.0300.0500.0380.1090.0510.0110.0721.0001.0000.5990.383
INTRSRT0.1200.4290.0360.0270.0230.0090.0370.0260.0190.0840.0590.0000.0990.7080.7000.6390.7190.7510.5720.6800.9040.0090.1820.8730.4450.0440.0040.0920.1500.0280.0460.0790.0600.1700.1590.0180.1311.0000.5991.0000.895
US_GINI0.0830.1420.0250.0210.0070.0080.0350.0180.0020.0710.0760.0000.0400.8370.8120.5630.7430.8390.8360.6220.8980.0150.0000.8600.1450.0170.0000.0490.1040.0150.0030.0500.0530.0590.1260.0190.0131.0000.3830.8951.000

Missing values

2023-10-22T16:20:52.392507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T16:20:54.043775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T16:20:55.507937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_dateMO_YRCPI_AVGINFLATIONINFLATION_CHGCSMR_SENTUNRATEINTRSRTGDPFUEL_PRCSCPI_HOTELSUS_GINIDIS_INC
0Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No DepositNaN0Transient0.000Check-Out7/1/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
1Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No DepositNaN0Transient0.000Check-Out7/1/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
2Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No DepositNaN0Transient75.000Check-Out7/2/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
3Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No Deposit304.00Transient75.000Check-Out7/2/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
4Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.00Transient98.001Check-Out7/3/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
5Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.00Transient98.001Check-Out7/3/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
6Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No DepositNaN0Transient107.000Check-Out7/3/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
7Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No Deposit303.00Transient103.001Check-Out7/3/20157-2015238.0341.80.093.15.30.7518306.960194.00.18756641.241355.0
8Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.00Transient82.001Canceled5/6/20155-2015237.0011.7-0.190.75.40.7518193.707202.60.18562041.241290.0
9Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.00Transient105.500Canceled4/22/20154-2015236.2221.80.095.95.40.7518193.707183.80.21669941.241248.0
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_dateMO_YRCPI_AVGINFLATIONINFLATION_CHGCSMR_SENTUNRATEINTRSRTGDPFUEL_PRCSCPI_HOTELSUS_GINIDIS_INC
119380City Hotel0442017August35311320.00SCDEUOnline TATA/TO000AA0No Deposit9.00Transient140.7501Check-Out9/4/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119381City Hotel01882017August35312320.00BBDEUDirectDirect000AA0No Deposit14.00Transient99.0000Check-Out9/5/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119382City Hotel01352017August35302430.00BBJPNOnline TATA/TO000GG0No Deposit7.00Transient209.0000Check-Out9/5/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119383City Hotel01642017August35312420.00BBDEUOffline TA/TOTA/TO000AA0No Deposit42.00Transient87.6000Check-Out9/6/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119384City Hotel0212017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.00Transient96.1402Check-Out9/6/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119385City Hotel0232017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.00Transient96.1400Check-Out9/6/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119386City Hotel01022017August35312530.00BBFRAOnline TATA/TO000EE0No Deposit9.00Transient225.4302Check-Out9/7/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119387City Hotel0342017August35312520.00BBDEUOnline TATA/TO000DD0No Deposit9.00Transient157.7104Check-Out9/7/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119388City Hotel01092017August35312520.00BBGBROnline TATA/TO000AA0No Deposit89.00Transient104.4000Check-Out9/7/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0
119389City Hotel02052017August35292720.00HBDEUOnline TATA/TO000AA0No Deposit9.00Transient151.2002Check-Out9/7/20179-2017246.4351.70.095.14.41.7519561.896204.20.15197241.242834.0

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_dateMO_YRCPI_AVGINFLATIONINFLATION_CHGCSMR_SENTUNRATEINTRSRTGDPFUEL_PRCSCPI_HOTELSUS_GINIDIS_INC# duplicates
5408City Hotel12772016November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaN0Transient100.000Canceled4/4/20164-2016238.9922.1-0.189.05.01.0018611.617123.60.17626541.141727.0180
4184City Hotel1682016February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.00Transient75.000Canceled1/6/20161-2016237.6522.20.192.05.01.0018425.306119.20.18998941.141827.0150
5078City Hotel11882016June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.039Transient130.000Canceled1/18/20161-2016237.6522.20.192.05.01.0018425.306119.20.18998941.141827.0109
4882City Hotel11582016May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.031Transient130.000Canceled1/18/20161-2016237.6522.20.192.05.01.0018425.306119.20.18998941.141827.0101
3852City Hotel1342015December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.00Transient90.000Canceled11/17/201511-2015238.0172.00.191.35.00.7518332.079167.40.18354741.241526.0100
3795City Hotel1282017March920320.00BBPRTGroupsTA/TO000AA0Non RefundNaN0Transient95.000Canceled2/2/20172-2017244.0062.2-0.196.34.71.2519148.194163.50.16575441.242274.099
3909City Hotel1382017January2140110.00BBPRTCorporateCorporate000AA0Non RefundNaN0Transient75.000Canceled12/7/201612-2016242.6372.20.198.24.71.2518968.041158.80.17450641.142013.099
4875City Hotel11562017April17260320.00BBPRTGroupsTA/TO000AA0Non Refund37.00Transient100.000Canceled11/21/201611-2016242.0262.10.093.84.91.0018968.041157.00.16383341.141952.099
4208City Hotel1712016June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.00Transient120.000Canceled4/27/20164-2016238.9922.1-0.189.05.01.0018611.617123.60.17626541.141727.089
4942City Hotel11662016November4510310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.00Transient110.000Canceled7/13/20167-2016240.1012.20.090.04.91.0018775.459157.60.18834841.141784.085